Load libraries
library(car)
library(knitr)
library(rmdformats)
library(ggplot2)
library(ggpubr)
library(GGally)
library(tidyverse)
library(lme4)
library(lmerTest)
library("MuMIn")
library(lmtest)
library(boot)
Read datasets
AllSubs_NeuralActivation <- read.csv('/Users/luisalvarez/Documents/GitHub/RM_Thesis_Neuroforecasting/ProcessedData/AllSubs_NeuralActivation_Aggregate_Combined_clean.csv')
AllSubs_NeuralActivation_Comedy <- read.csv('/Users/luisalvarez/Documents/GitHub/RM_Thesis_Neuroforecasting/ProcessedData/AllSubs_NeuralActivation_Aggregate_Combined_Comedy_clean.csv')
AllSubs_NeuralActivation_Horror <- read.csv('/Users/luisalvarez/Documents/GitHub/RM_Thesis_Neuroforecasting/ProcessedData/AllSubs_NeuralActivation_Aggregate_Combined_Horror_clean.csv')
Notes:
- Have note removed outliers from data.
Create data frames for each model.
# Define aggregate variables.
All_Gross_W1_log <- log(AllSubs_NeuralActivation$Gross_US_W1_num)
All_Theaters_W1 <- AllSubs_NeuralActivation$Theaters_US_W1_num
Comedy_Gross_W1_log <- log(AllSubs_NeuralActivation_Comedy$Gross_US_W1_num)
Comedy_Theaters_W1 <- AllSubs_NeuralActivation_Comedy$Theaters_US_W1_num
Horror_Gross_W1_log <- log(AllSubs_NeuralActivation_Horror$Gross_US_W1_num)
Horror_Theaters_W1 <- AllSubs_NeuralActivation_Horror$Theaters_US_W1_num
M1_df <- data.frame(All_Gross_W1_log, All_Theaters_W1)
M1_C_df <- data.frame(Comedy_Gross_W1_log, Comedy_Theaters_W1)
M1_H_df <- data.frame(Horror_Gross_W1_log, Horror_Theaters_W1)
# Define affect variables.
All_PA <- AllSubs_NeuralActivation$Pos_arousal_scaled
All_NA <- AllSubs_NeuralActivation$Neg_arousal_scaled
Comedy_PA <- AllSubs_NeuralActivation_Comedy$Pos_arousal_scaled
Comedy_NA <- AllSubs_NeuralActivation_Comedy$Neg_arousal_scaled
Horror_PA <- AllSubs_NeuralActivation_Horror$Pos_arousal_scaled
Horror_NA <- AllSubs_NeuralActivation_Horror$Neg_arousal_scaled
M2_df <- data.frame(All_Gross_W1_log, All_PA, All_NA)
M2_C_df <- data.frame(Comedy_Gross_W1_log, Comedy_PA, Comedy_NA)
M2_H_df <- data.frame(Horror_Gross_W1_log, Horror_PA, Horror_NA)
# Define ISC variables.
All_NAcc_ISC <- AllSubs_NeuralActivation$NAcc_ISC
All_AIns_ISC <- AllSubs_NeuralActivation$AIns_ISC
All_MPFC_ISC <- AllSubs_NeuralActivation$MPFC_ISC
Comedy_NAcc_ISC <- AllSubs_NeuralActivation_Comedy$NAcc_ISC
Comedy_AIns_ISC <- AllSubs_NeuralActivation_Comedy$AIns_ISC
Comedy_MPFC_ISC <- AllSubs_NeuralActivation_Comedy$MPFC_ISC
Horror_NAcc_ISC <- AllSubs_NeuralActivation_Horror$NAcc_ISC
Horror_AIns_ISC <- AllSubs_NeuralActivation_Horror$AIns_ISC
Horror_MPFC_ISC <- AllSubs_NeuralActivation_Horror$MPFC_ISC
# Define models.
M4_df <- data.frame(All_NAcc_ISC, All_AIns_ISC, All_MPFC_ISC)
M4_C_df <- data.frame(Comedy_NAcc_ISC, Comedy_AIns_ISC, Comedy_MPFC_ISC)
M4_H_df <- data.frame(Horror_NAcc_ISC, Horror_AIns_ISC, Horror_MPFC_ISC)
M5_df <- data.frame(All_Gross_W1_log, All_PA, All_NA, All_NAcc_ISC, All_AIns_ISC, All_MPFC_ISC)
M5_C_df <- data.frame(Comedy_Gross_W1_log, Comedy_PA, Comedy_NA, Comedy_NAcc_ISC, Comedy_AIns_ISC, Comedy_MPFC_ISC)
M5_H_df <- data.frame(Horror_Gross_W1_log, Horror_PA, Horror_NA, Horror_NAcc_ISC, Horror_AIns_ISC, Horror_MPFC_ISC)
# Define whole variables.
All_NAcc_whole <- AllSubs_NeuralActivation$NAcc_whole
All_AIns_whole <- AllSubs_NeuralActivation$AIns_whole
All_MPFC_whole <- AllSubs_NeuralActivation$MPFC_whole
Comedy_NAcc_whole <- AllSubs_NeuralActivation_Comedy$NAcc_whole
Comedy_AIns_whole <- AllSubs_NeuralActivation_Comedy$AIns_whole
Comedy_MPFC_whole <- AllSubs_NeuralActivation_Comedy$MPFC_whole
Horror_NAcc_whole <- AllSubs_NeuralActivation_Horror$NAcc_whole
Horror_AIns_whole <- AllSubs_NeuralActivation_Horror$AIns_whole
Horror_MPFC_whole <- AllSubs_NeuralActivation_Horror$MPFC_whole
# Define models.
M6_df <- data.frame(All_NAcc_whole, All_AIns_whole, All_MPFC_whole)
M6_C_df <- data.frame(Comedy_NAcc_whole, Comedy_AIns_whole, Comedy_MPFC_whole)
M6_H_df <- data.frame(Horror_NAcc_whole, Horror_AIns_whole, Horror_MPFC_whole)
M7_df <- data.frame(All_Gross_W1_log, All_PA, All_NA, All_NAcc_whole, All_AIns_whole, All_MPFC_whole)
M7_C_df <- data.frame(Comedy_Gross_W1_log, Comedy_PA, Comedy_NA, Comedy_NAcc_whole,
Comedy_AIns_whole, Comedy_MPFC_whole)
M7_H_df <- data.frame(Horror_Gross_W1_log, Horror_PA, Horror_NA, Horror_NAcc_whole,
Horror_AIns_whole, Horror_MPFC_whole)
# Define onset variables.
All_NAcc_onset <- AllSubs_NeuralActivation$NAcc_onset
All_AIns_onset <- AllSubs_NeuralActivation$AIns_onset
All_MPFC_onset <- AllSubs_NeuralActivation$MPFC_onset
Comedy_NAcc_onset <- AllSubs_NeuralActivation_Comedy$NAcc_onset
Comedy_AIns_onset <- AllSubs_NeuralActivation_Comedy$AIns_onset
Comedy_MPFC_onset <- AllSubs_NeuralActivation_Comedy$MPFC_onset
Horror_NAcc_onset <- AllSubs_NeuralActivation_Horror$NAcc_onset
Horror_AIns_onset <- AllSubs_NeuralActivation_Horror$AIns_onset
Horror_MPFC_onset <- AllSubs_NeuralActivation_Horror$MPFC_onset
# Define models.
M8_df <- data.frame(All_NAcc_onset, All_AIns_onset, All_MPFC_onset)
M8_C_df <- data.frame(Comedy_NAcc_onset, Comedy_AIns_onset, Comedy_MPFC_onset)
M8_H_df <- data.frame(Horror_NAcc_onset, Horror_AIns_onset, Horror_MPFC_onset)
M9_df <- data.frame(All_Gross_W1_log, All_PA, All_NA, All_NAcc_onset, All_AIns_onset, All_MPFC_onset)
M9_C_df <- data.frame(Comedy_Gross_W1_log, Comedy_PA, Comedy_NA, Comedy_NAcc_onset,
Comedy_AIns_onset, Comedy_MPFC_onset)
M9_H_df <- data.frame(Horror_Gross_W1_log, Horror_PA, Horror_NA, Horror_NAcc_onset,
Horror_AIns_onset, Horror_MPFC_onset)
# Define middle variables.
All_NAcc_middle <- AllSubs_NeuralActivation$NAcc_middle
All_AIns_middle <- AllSubs_NeuralActivation$AIns_middle
All_MPFC_middle <- AllSubs_NeuralActivation$MPFC_middle
Comedy_NAcc_middle <- AllSubs_NeuralActivation_Comedy$NAcc_middle
Comedy_AIns_middle <- AllSubs_NeuralActivation_Comedy$AIns_middle
Comedy_MPFC_middle <- AllSubs_NeuralActivation_Comedy$MPFC_middle
Horror_NAcc_middle <- AllSubs_NeuralActivation_Horror$NAcc_middle
Horror_AIns_middle <- AllSubs_NeuralActivation_Horror$AIns_middle
Horror_MPFC_middle <- AllSubs_NeuralActivation_Horror$MPFC_middle
# Define models.
M10_df <- data.frame(All_NAcc_middle, All_AIns_middle, All_MPFC_middle)
M10_C_df <- data.frame(Comedy_NAcc_middle, Comedy_AIns_middle, Comedy_MPFC_middle)
M10_H_df <- data.frame(Horror_NAcc_middle, Horror_AIns_middle, Horror_MPFC_middle)
M11_df <- data.frame(All_Gross_W1_log, All_PA, All_NA, All_NAcc_middle, All_AIns_middle, All_MPFC_middle)
M11_C_df <- data.frame(Comedy_Gross_W1_log, Comedy_PA, Comedy_NA, Comedy_NAcc_middle,
Comedy_AIns_middle, Comedy_MPFC_middle)
M11_H_df <- data.frame(Horror_Gross_W1_log, Horror_PA, Horror_NA, Horror_NAcc_middle,
Horror_AIns_middle, Horror_MPFC_middle)
# Define middle variables.
All_NAcc_offset <- AllSubs_NeuralActivation$NAcc_offset
All_AIns_offset <- AllSubs_NeuralActivation$AIns_offset
All_MPFC_offset <- AllSubs_NeuralActivation$MPFC_offset
Comedy_NAcc_offset <- AllSubs_NeuralActivation_Comedy$NAcc_offset
Comedy_AIns_offset <- AllSubs_NeuralActivation_Comedy$AIns_offset
Comedy_MPFC_offset <- AllSubs_NeuralActivation_Comedy$MPFC_offset
Horror_NAcc_offset <- AllSubs_NeuralActivation_Horror$NAcc_offset
Horror_AIns_offset <- AllSubs_NeuralActivation_Horror$AIns_offset
Horror_MPFC_offset <- AllSubs_NeuralActivation_Horror$MPFC_offset
# Define models.
M12_df <- data.frame(All_NAcc_offset, All_AIns_offset, All_MPFC_offset)
M12_C_df <- data.frame(Comedy_NAcc_offset, Comedy_AIns_offset, Comedy_MPFC_offset)
M12_H_df <- data.frame(Horror_NAcc_offset, Horror_AIns_offset, Horror_MPFC_offset)
M13_df <- data.frame(All_Gross_W1_log, All_PA, All_NA, All_NAcc_offset, All_AIns_offset, All_MPFC_offset)
M13_C_df <- data.frame(Comedy_Gross_W1_log, Comedy_PA, Comedy_NA, Comedy_NAcc_offset,
Comedy_AIns_offset, Comedy_MPFC_offset)
M13_H_df <- data.frame(Horror_Gross_W1_log, Horror_PA, Horror_NA, Horror_NAcc_offset,
Horror_AIns_offset, Horror_MPFC_offset)
M14_df <- data.frame(All_Gross_W1_log, All_PA, All_NA, All_NAcc_onset, All_AIns_middle, All_MPFC_offset)
M14_C_df <- data.frame(Comedy_Gross_W1_log, Comedy_PA, Comedy_NA, Comedy_NAcc_onset,
Comedy_AIns_middle, Comedy_MPFC_offset)
M14_H_df <- data.frame(Horror_Gross_W1_log, Horror_PA, Horror_NA, Horror_NAcc_onset,
Horror_AIns_middle, Horror_MPFC_offset)
Neuroforecasting: First Week US.
M1: Behavioral data
Call:
lm(formula = log(Gross_US_W1_num) ~ Type + scale(Theaters_US_W1_num) +
Type:scale(Theaters_US_W1_num), data = AllSubs_NeuralActivation %>%
mutate(Type = factor(Type, levels = c("horror", "comedy"))))
Residuals:
Min 1Q Median 3Q Max
-1.55922 -0.28515 0.02387 0.33475 1.38066
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 16.4641 0.2010 81.928 < 2e-16 ***
Typecomedy -0.5646 0.2655 -2.127 0.04310 *
scale(Theaters_US_W1_num) 1.5282 0.4206 3.633 0.00121 **
Typecomedy:scale(Theaters_US_W1_num) -0.3868 0.4422 -0.875 0.38980
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.688 on 26 degrees of freedom
Multiple R-squared: 0.7944, Adjusted R-squared: 0.7706
F-statistic: 33.48 on 3 and 26 DF, p-value: 4.425e-09
R2m R2c
[1,] 0.7759523 0.7759523
[1] 68.40126



M2: Affective data alone
Call:
lm(formula = log(Gross_US_W1_num) ~ Type + scale(Pos_arousal_scaled) +
scale(Neg_arousal_scaled) + Type:scale(Pos_arousal_scaled) +
Type:scale(Neg_arousal_scaled), data = AllSubs_NeuralActivation %>%
mutate(Type = factor(Type, levels = c("horror", "comedy"))))
Residuals:
Min 1Q Median 3Q Max
-4.1431 -0.4889 0.1215 0.9237 1.9269
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 17.0177 1.1362 14.977 1.12e-13 ***
Typecomedy -0.2736 1.8589 -0.147 0.884
scale(Pos_arousal_scaled) -0.1825 0.8610 -0.212 0.834
scale(Neg_arousal_scaled) -0.3956 0.7695 -0.514 0.612
Typecomedy:scale(Pos_arousal_scaled) 0.4911 0.9404 0.522 0.606
Typecomedy:scale(Neg_arousal_scaled) 1.8368 1.8219 1.008 0.323
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 1.411 on 24 degrees of freedom
Multiple R-squared: 0.2017, Adjusted R-squared: 0.03533
F-statistic: 1.212 on 5 and 24 DF, p-value: 0.3335
R2m R2c
[1,] 0.1728986 0.1728986
[1] 113.0942



M3: Aggregate and affective data alone
Call:
lm(formula = log(Gross_US_W1_num) ~ Type + scale(Pos_arousal_scaled) +
scale(Neg_arousal_scaled) + Type:scale(Pos_arousal_scaled) +
Type:scale(Neg_arousal_scaled), data = AllSubs_NeuralActivation %>%
mutate(Type = factor(Type, levels = c("horror", "comedy"))))
Residuals:
Min 1Q Median 3Q Max
-4.1431 -0.4889 0.1215 0.9237 1.9269
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 17.0177 1.1362 14.977 1.12e-13 ***
Typecomedy -0.2736 1.8589 -0.147 0.884
scale(Pos_arousal_scaled) -0.1825 0.8610 -0.212 0.834
scale(Neg_arousal_scaled) -0.3956 0.7695 -0.514 0.612
Typecomedy:scale(Pos_arousal_scaled) 0.4911 0.9404 0.522 0.606
Typecomedy:scale(Neg_arousal_scaled) 1.8368 1.8219 1.008 0.323
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 1.411 on 24 degrees of freedom
Multiple R-squared: 0.2017, Adjusted R-squared: 0.03533
F-statistic: 1.212 on 5 and 24 DF, p-value: 0.3335
R2m R2c
[1,] 0.1728986 0.1728986
[1] 113.0942
M4: ISC data alone
Call:
lm(formula = log(Gross_US_W1_num) ~ Type + +scale(NAcc_ISC) +
scale(AIns_ISC) + scale(MPFC_ISC) + Type:scale(NAcc_ISC) +
Type:scale(AIns_ISC) + Type:scale(MPFC_ISC), data = AllSubs_NeuralActivation %>%
mutate(Type = factor(Type, levels = c("horror", "comedy"))))
Residuals:
Min 1Q Median 3Q Max
-4.5626 -0.3083 0.2310 0.5370 1.9757
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 16.70980 0.40320 41.443 <2e-16 ***
Typecomedy -0.99626 0.54755 -1.819 0.0825 .
scale(NAcc_ISC) 0.61228 0.56292 1.088 0.2885
scale(AIns_ISC) -0.12105 0.38625 -0.313 0.7569
scale(MPFC_ISC) 0.29369 0.53461 0.549 0.5883
Typecomedy:scale(NAcc_ISC) -0.80448 0.66711 -1.206 0.2407
Typecomedy:scale(AIns_ISC) 0.38701 0.62113 0.623 0.5396
Typecomedy:scale(MPFC_ISC) -0.09906 0.64873 -0.153 0.8800
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 1.453 on 22 degrees of freedom
Multiple R-squared: 0.2241, Adjusted R-squared: -0.02275
F-statistic: 0.9078 on 7 and 22 DF, p-value: 0.5184
R2m R2c
[1,] 0.1797455 0.1797455
[1] 116.2379



M5: ISC data + affective data + behavioral data
Call:
lm(formula = log(Gross_US_W1_num) ~ Type + scale(Theaters_US_W1_num) +
scale(Pos_arousal_scaled) + scale(Neg_arousal_scaled) + scale(NAcc_ISC) +
scale(AIns_ISC) + scale(MPFC_ISC) + Type:scale(Theaters_US_W1_num) +
Type:scale(Pos_arousal_scaled) + Type:scale(Neg_arousal_scaled) +
Type:scale(NAcc_ISC) + Type:scale(AIns_ISC) + Type:scale(MPFC_ISC),
data = AllSubs_NeuralActivation %>% mutate(Type = factor(Type,
levels = c("horror", "comedy"))))
Residuals:
Min 1Q Median 3Q Max
-0.86763 -0.24968 -0.00754 0.28252 1.12778
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 16.01970 0.56278 28.465 3.92e-15 ***
Typecomedy 1.95895 0.89449 2.190 0.04368 *
scale(Theaters_US_W1_num) 1.30716 0.44394 2.944 0.00952 **
scale(Pos_arousal_scaled) -0.90672 0.40708 -2.227 0.04063 *
scale(Neg_arousal_scaled) -0.25800 0.37397 -0.690 0.50015
scale(NAcc_ISC) 0.23035 0.26084 0.883 0.39026
scale(AIns_ISC) -0.28392 0.16844 -1.686 0.11127
scale(MPFC_ISC) 0.60359 0.26119 2.311 0.03449 *
Typecomedy:scale(Theaters_US_W1_num) -0.02941 0.46422 -0.063 0.95027
Typecomedy:scale(Pos_arousal_scaled) 0.68765 0.45661 1.506 0.15155
Typecomedy:scale(Neg_arousal_scaled) 2.58832 0.87119 2.971 0.00901 **
Typecomedy:scale(NAcc_ISC) 0.07230 0.31912 0.227 0.82363
Typecomedy:scale(AIns_ISC) 0.18418 0.28584 0.644 0.52849
Typecomedy:scale(MPFC_ISC) -0.97877 0.31329 -3.124 0.00654 **
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.5868 on 16 degrees of freedom
Multiple R-squared: 0.9079, Adjusted R-squared: 0.8331
F-statistic: 12.14 on 13 and 16 DF, p-value: 6.43e-06
R2m R2c
[1,] 0.8447398 0.8447398
[1] 64.29403



M6: Neural whole data alone
Call:
lm(formula = log(Gross_US_W1_num) ~ Type + +scale(NAcc_whole) +
scale(AIns_whole) + scale(MPFC_whole) + Type:scale(NAcc_whole) +
Type:scale(AIns_whole) + Type:scale(MPFC_whole), data = AllSubs_NeuralActivation %>%
mutate(Type = factor(Type, levels = c("horror", "comedy"))))
Residuals:
Min 1Q Median 3Q Max
-3.6829 -0.3831 0.1901 0.6917 2.0438
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 16.5732 0.5184 31.967 <2e-16 ***
Typecomedy -0.7712 0.7399 -1.042 0.309
scale(NAcc_whole) -0.5606 0.5368 -1.044 0.308
scale(AIns_whole) 0.5299 0.6150 0.862 0.398
scale(MPFC_whole) 0.1706 0.5180 0.329 0.745
Typecomedy:scale(NAcc_whole) 0.3765 0.7021 0.536 0.597
Typecomedy:scale(AIns_whole) -0.2614 0.9131 -0.286 0.777
Typecomedy:scale(MPFC_whole) 0.2835 0.6416 0.442 0.663
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 1.44 on 22 degrees of freedom
Multiple R-squared: 0.2374, Adjusted R-squared: -0.005294
F-statistic: 0.9782 on 7 and 22 DF, p-value: 0.4714
R2m R2c
[1,] 0.1910128 0.1910128
[1] 115.7214



M7: Neural whole data + affective data + behavioral data
Call:
lm(formula = log(Gross_US_W1_num) ~ Type + scale(Theaters_US_W1_num) +
scale(Pos_arousal_scaled) + scale(Neg_arousal_scaled) + scale(NAcc_whole) +
scale(AIns_whole) + scale(MPFC_whole) + Type:scale(Theaters_US_W1_num) +
Type:scale(Pos_arousal_scaled) + Type:scale(Neg_arousal_scaled) +
Type:scale(NAcc_whole) + Type:scale(AIns_whole) + Type:scale(MPFC_whole),
data = AllSubs_NeuralActivation %>% mutate(Type = factor(Type,
levels = c("horror", "comedy"))))
Residuals:
Min 1Q Median 3Q Max
-0.99456 -0.27314 -0.01657 0.28123 0.96246
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 16.25722 0.60777 26.749 1.04e-14 ***
Typecomedy 1.57410 0.91504 1.720 0.1047
scale(Theaters_US_W1_num) 1.52817 0.41444 3.687 0.0020 **
scale(Pos_arousal_scaled) -0.20928 0.58243 -0.359 0.7241
scale(Neg_arousal_scaled) -0.09669 0.38485 -0.251 0.8048
scale(NAcc_whole) -0.14503 0.26272 -0.552 0.5885
scale(AIns_whole) 0.28367 0.28207 1.006 0.3295
scale(MPFC_whole) -0.08670 0.32947 -0.263 0.7958
Typecomedy:scale(Theaters_US_W1_num) -0.38738 0.43351 -0.894 0.3848
Typecomedy:scale(Pos_arousal_scaled) 0.27624 0.61052 0.452 0.6570
Typecomedy:scale(Neg_arousal_scaled) 2.45693 0.88987 2.761 0.0139 *
Typecomedy:scale(NAcc_whole) 0.51907 0.33703 1.540 0.1431
Typecomedy:scale(AIns_whole) -0.47906 0.41378 -1.158 0.2640
Typecomedy:scale(MPFC_whole) 0.38309 0.37290 1.027 0.3196
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.5955 on 16 degrees of freedom
Multiple R-squared: 0.9052, Adjusted R-squared: 0.8281
F-statistic: 11.75 on 13 and 16 DF, p-value: 8.023e-06
R2m R2c
[1,] 0.8404262 0.8404262
[1] 65.17857



M8: Neural onset data alone
Call:
lm(formula = log(Gross_US_W1_num) ~ Type + +scale(NAcc_onset) +
scale(AIns_onset) + scale(MPFC_onset) + Type:scale(NAcc_onset) +
Type:scale(AIns_onset) + Type:scale(MPFC_onset), data = AllSubs_NeuralActivation %>%
mutate(Type = factor(Type, levels = c("horror", "comedy"))))
Residuals:
Min 1Q Median 3Q Max
-3.6998 -0.4321 0.1720 0.8185 1.6881
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 16.9078 0.4518 37.427 <2e-16 ***
Typecomedy -1.6161 0.5897 -2.741 0.0119 *
scale(NAcc_onset) -0.3234 0.5216 -0.620 0.5415
scale(AIns_onset) -0.2077 0.5842 -0.356 0.7256
scale(MPFC_onset) 0.1921 0.4738 0.405 0.6891
Typecomedy:scale(NAcc_onset) 0.4906 0.6429 0.763 0.4535
Typecomedy:scale(AIns_onset) -0.7656 0.7631 -1.003 0.3266
Typecomedy:scale(MPFC_onset) 1.0559 0.6864 1.538 0.1382
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 1.335 on 22 degrees of freedom
Multiple R-squared: 0.3447, Adjusted R-squared: 0.1362
F-statistic: 1.653 on 7 and 22 DF, p-value: 0.1729
R2m R2c
[1,] 0.2852503 0.2852503
[1] 111.1697



M9: Neural onset data + affective data + behavioral data
Call:
lm(formula = log(Gross_US_W1_num) ~ Type + scale(Theaters_US_W1_num) +
scale(Pos_arousal_scaled) + scale(Neg_arousal_scaled) + scale(NAcc_onset) +
scale(AIns_onset) + scale(MPFC_onset) + Type:scale(Theaters_US_W1_num) +
Type:scale(Pos_arousal_scaled) + Type:scale(Neg_arousal_scaled) +
Type:scale(NAcc_onset) + Type:scale(AIns_onset) + Type:scale(MPFC_onset),
data = AllSubs_NeuralActivation %>% mutate(Type = factor(Type,
levels = c("horror", "comedy"))))
Residuals:
Min 1Q Median 3Q Max
-0.90801 -0.39211 0.06306 0.33282 1.20478
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 17.302775 0.844057 20.500 6.54e-13 ***
Typecomedy -0.076805 1.202093 -0.064 0.94985
scale(Theaters_US_W1_num) 1.562535 0.425321 3.674 0.00205 **
scale(Pos_arousal_scaled) -0.366374 0.499891 -0.733 0.47422
scale(Neg_arousal_scaled) -0.741677 0.523649 -1.416 0.17584
scale(NAcc_onset) -0.342762 0.271261 -1.264 0.22448
scale(AIns_onset) -0.841501 0.392021 -2.147 0.04750 *
scale(MPFC_onset) 0.268483 0.256281 1.048 0.31038
Typecomedy:scale(Theaters_US_W1_num) -0.467642 0.452099 -1.034 0.31634
Typecomedy:scale(Pos_arousal_scaled) 0.205970 0.548225 0.376 0.71207
Typecomedy:scale(Neg_arousal_scaled) 2.338685 1.039689 2.249 0.03892 *
Typecomedy:scale(NAcc_onset) 0.520227 0.368203 1.413 0.17685
Typecomedy:scale(AIns_onset) 0.477460 0.473021 1.009 0.32781
Typecomedy:scale(MPFC_onset) -0.009214 0.379665 -0.024 0.98094
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.6614 on 16 degrees of freedom
Multiple R-squared: 0.883, Adjusted R-squared: 0.788
F-statistic: 9.292 on 13 and 16 DF, p-value: 3.817e-05
R2m R2c
[1,] 0.8064055 0.8064055
[1] 71.47321



M10: Neural middle data alone
Call:
lm(formula = log(Gross_US_W1_num) ~ Type + +scale(NAcc_middle) +
scale(AIns_middle) + scale(MPFC_middle) + Type:scale(NAcc_middle) +
Type:scale(AIns_middle) + Type:scale(MPFC_middle), data = AllSubs_NeuralActivation %>%
mutate(Type = factor(Type, levels = c("horror", "comedy"))))
Residuals:
Min 1Q Median 3Q Max
-4.0479 -0.3572 0.1014 0.7554 1.8228
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 16.77683 0.46169 36.338 <2e-16 ***
Typecomedy -0.44119 0.65006 -0.679 0.504
scale(NAcc_middle) -0.37128 0.60411 -0.615 0.545
scale(AIns_middle) 0.13702 0.44866 0.305 0.763
scale(MPFC_middle) -0.27542 0.41798 -0.659 0.517
Typecomedy:scale(NAcc_middle) 0.40053 0.72039 0.556 0.584
Typecomedy:scale(AIns_middle) 0.98953 0.72471 1.365 0.186
Typecomedy:scale(MPFC_middle) -0.02962 0.60640 -0.049 0.961
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 1.367 on 22 degrees of freedom
Multiple R-squared: 0.3126, Adjusted R-squared: 0.09385
F-statistic: 1.429 on 7 and 22 DF, p-value: 0.2438
R2m R2c
[1,] 0.2564756 0.2564756
[1] 112.6066



M11: Neural middle data + affective data + behavioral data
Call:
lm(formula = log(Gross_US_W1_num) ~ Type + scale(Theaters_US_W1_num) +
scale(Pos_arousal_scaled) + scale(Neg_arousal_scaled) + scale(NAcc_middle) +
scale(AIns_middle) + scale(MPFC_middle) + Type:scale(Theaters_US_W1_num) +
Type:scale(Pos_arousal_scaled) + Type:scale(Neg_arousal_scaled) +
Type:scale(NAcc_middle) + Type:scale(AIns_middle) + Type:scale(MPFC_middle),
data = AllSubs_NeuralActivation %>% mutate(Type = factor(Type,
levels = c("horror", "comedy"))))
Residuals:
Min 1Q Median 3Q Max
-0.90123 -0.34967 0.06124 0.32376 1.04536
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 16.21572 0.60396 26.849 9.8e-15 ***
Typecomedy 0.84466 0.91724 0.921 0.37079
scale(Theaters_US_W1_num) 1.81371 0.48933 3.707 0.00192 **
scale(Pos_arousal_scaled) -0.35687 0.43224 -0.826 0.42115
scale(Neg_arousal_scaled) -0.30347 0.42833 -0.708 0.48884
scale(NAcc_middle) 0.36408 0.39195 0.929 0.36675
scale(AIns_middle) 0.22775 0.21781 1.046 0.31127
scale(MPFC_middle) -0.10071 0.21057 -0.478 0.63893
Typecomedy:scale(Theaters_US_W1_num) -0.65853 0.50875 -1.294 0.21390
Typecomedy:scale(Pos_arousal_scaled) 0.21594 0.49100 0.440 0.66596
Typecomedy:scale(Neg_arousal_scaled) 1.31128 0.98801 1.327 0.20307
Typecomedy:scale(NAcc_middle) -0.04305 0.43809 -0.098 0.92295
Typecomedy:scale(AIns_middle) 0.03310 0.45306 0.073 0.94267
Typecomedy:scale(MPFC_middle) -0.13681 0.31461 -0.435 0.66947
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.6424 on 16 degrees of freedom
Multiple R-squared: 0.8897, Adjusted R-squared: 0.8
F-statistic: 9.924 on 13 and 16 DF, p-value: 2.481e-05
R2m R2c
[1,] 0.8164716 0.8164716
[1] 69.72344



M12: Neural offset data alone
Call:
lm(formula = log(Gross_US_W1_num) ~ Type + +scale(NAcc_offset) +
scale(AIns_offset) + scale(MPFC_offset) + Type:scale(NAcc_offset) +
Type:scale(AIns_offset) + Type:scale(MPFC_offset), data = AllSubs_NeuralActivation %>%
mutate(Type = factor(Type, levels = c("horror", "comedy"))))
Residuals:
Min 1Q Median 3Q Max
-3.6743 -0.3508 0.1999 0.6837 2.0821
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 16.6822 0.4184 39.867 <2e-16 ***
Typecomedy -1.1003 0.5997 -1.835 0.0801 .
scale(NAcc_offset) -0.2359 0.4577 -0.516 0.6113
scale(AIns_offset) 0.2008 0.3980 0.505 0.6189
scale(MPFC_offset) 0.2403 0.5422 0.443 0.6619
Typecomedy:scale(NAcc_offset) -0.1149 0.7216 -0.159 0.8750
Typecomedy:scale(AIns_offset) -0.5850 0.8097 -0.722 0.4776
Typecomedy:scale(MPFC_offset) 0.2360 0.6859 0.344 0.7340
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 1.467 on 22 degrees of freedom
Multiple R-squared: 0.2093, Adjusted R-squared: -0.04228
F-statistic: 0.8319 on 7 and 22 DF, p-value: 0.5723
R2m R2c
[1,] 0.1672302 0.1672302
[1] 116.8054



M13: Neural offset data + affective data + behavioral data
Call:
lm(formula = log(Gross_US_W1_num) ~ Type + scale(Theaters_US_W1_num) +
scale(Pos_arousal_scaled) + scale(Neg_arousal_scaled) + scale(NAcc_offset) +
scale(AIns_offset) + scale(MPFC_offset) + Type:scale(Theaters_US_W1_num) +
Type:scale(Pos_arousal_scaled) + Type:scale(Neg_arousal_scaled) +
Type:scale(NAcc_offset) + Type:scale(AIns_offset) + Type:scale(MPFC_offset),
data = AllSubs_NeuralActivation %>% mutate(Type = factor(Type,
levels = c("horror", "comedy"))))
Residuals:
Min 1Q Median 3Q Max
-0.83310 -0.25490 -0.02203 0.31792 0.94449
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 16.392380 0.504429 32.497 4.87e-16 ***
Typecomedy 0.871610 0.745363 1.169 0.259378
scale(Theaters_US_W1_num) 1.958277 0.478836 4.090 0.000855 ***
scale(Pos_arousal_scaled) 0.007493 0.426807 0.018 0.986211
scale(Neg_arousal_scaled) -0.081703 0.441972 -0.185 0.855661
scale(NAcc_offset) -0.150464 0.174607 -0.862 0.401568
scale(AIns_offset) 0.269710 0.163034 1.654 0.117544
scale(MPFC_offset) -0.397626 0.347125 -1.145 0.268855
Typecomedy:scale(Theaters_US_W1_num) -0.783077 0.491361 -1.594 0.130566
Typecomedy:scale(Pos_arousal_scaled) 0.160197 0.457094 0.350 0.730558
Typecomedy:scale(Neg_arousal_scaled) 1.762680 0.779570 2.261 0.038040 *
Typecomedy:scale(NAcc_offset) 0.030317 0.267425 0.113 0.911151
Typecomedy:scale(AIns_offset) -0.227606 0.315011 -0.723 0.480395
Typecomedy:scale(MPFC_offset) 0.950272 0.379822 2.502 0.023585 *
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.5168 on 16 degrees of freedom
Multiple R-squared: 0.9286, Adjusted R-squared: 0.8706
F-statistic: 16 on 13 and 16 DF, p-value: 9.396e-07
R2m R2c
[1,] 0.8776506 0.8776506
[1] 56.67541



M14: Sequence model
Call:
lm(formula = log(Gross_US_W1_num) ~ Type + scale(Theaters_US_W1_num) +
scale(Pos_arousal_scaled) + scale(Neg_arousal_scaled) + scale(NAcc_onset) +
scale(AIns_middle) + scale(MPFC_offset) + Type:scale(Pos_arousal_scaled) +
Type:scale(Neg_arousal_scaled) + Type:scale(NAcc_onset) +
Type:scale(AIns_middle) + Type:scale(MPFC_offset), data = AllSubs_NeuralActivation %>%
mutate(Type = factor(Type, levels = c("horror", "comedy"))))
Residuals:
Min 1Q Median 3Q Max
-1.00369 -0.10391 0.00625 0.19037 0.76465
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 16.50986 0.39356 41.950 < 2e-16 ***
Typecomedy 1.49474 0.69392 2.154 0.045875 *
scale(Theaters_US_W1_num) 1.20671 0.09297 12.979 3e-10 ***
scale(Pos_arousal_scaled) -0.26824 0.33773 -0.794 0.437992
scale(Neg_arousal_scaled) -0.38981 0.27581 -1.413 0.175606
scale(NAcc_onset) -0.36685 0.19239 -1.907 0.073595 .
scale(AIns_middle) 0.41837 0.15054 2.779 0.012859 *
scale(MPFC_offset) 0.11172 0.23432 0.477 0.639590
Typecomedy:scale(Pos_arousal_scaled) 0.35027 0.37689 0.929 0.365707
Typecomedy:scale(Neg_arousal_scaled) 2.85753 0.71248 4.011 0.000906 ***
Typecomedy:scale(NAcc_onset) 0.69729 0.24036 2.901 0.009942 **
Typecomedy:scale(AIns_middle) -0.46369 0.26794 -1.731 0.101635
Typecomedy:scale(MPFC_offset) 0.52593 0.27348 1.923 0.071384 .
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.4455 on 17 degrees of freedom
Multiple R-squared: 0.9436, Adjusted R-squared: 0.9038
F-statistic: 23.71 on 12 and 17 DF, p-value: 3.021e-08
R2m R2c
[1,] 0.9074989 0.9074989
[1] 47.58297
there are higher-order terms (interactions) in this model
consider setting type = 'predictor'; see ?vif
Type scale(Theaters_US_W1_num) scale(Pos_arousal_scaled)
18.115472 1.263053 16.666114
scale(Neg_arousal_scaled) scale(NAcc_onset) scale(AIns_middle)
11.115527 5.408594 3.311431
scale(MPFC_offset) Type:scale(Pos_arousal_scaled) Type:scale(Neg_arousal_scaled)
8.023124 11.875668 15.574833
Type:scale(NAcc_onset) Type:scale(AIns_middle) Type:scale(MPFC_offset)
5.464800 3.321676 7.912335
---
title: "R Notebook"
output: html_notebook
---

```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE)
```

# Load libraries
```{r}
library(car)
library(knitr)
library(rmdformats)
library(ggplot2)
library(ggpubr)
library(GGally)
```


```{r, warning = FALSE, message = FALSE}
library(tidyverse)
library(lme4)
library(lmerTest)
library("MuMIn")
library(lmtest)
library(boot)
```

# Read datasets
```{r}
AllSubs_NeuralActivation <- read.csv('/Users/luisalvarez/Documents/GitHub/RM_Thesis_Neuroforecasting/ProcessedData/AllSubs_NeuralActivation_Aggregate_Combined_clean.csv')

AllSubs_NeuralActivation_Comedy <- read.csv('/Users/luisalvarez/Documents/GitHub/RM_Thesis_Neuroforecasting/ProcessedData/AllSubs_NeuralActivation_Aggregate_Combined_Comedy_clean.csv')

AllSubs_NeuralActivation_Horror <- read.csv('/Users/luisalvarez/Documents/GitHub/RM_Thesis_Neuroforecasting/ProcessedData/AllSubs_NeuralActivation_Aggregate_Combined_Horror_clean.csv')

```
# Notes: 
 - Have note removed outliers from data.

# Create data frames for each model.
```{r}
# Define aggregate variables. 
All_Gross_W1_log <- log(AllSubs_NeuralActivation$Gross_US_W1_num)
All_Theaters_W1 <- AllSubs_NeuralActivation$Theaters_US_W1_num

Comedy_Gross_W1_log <- log(AllSubs_NeuralActivation_Comedy$Gross_US_W1_num)
Comedy_Theaters_W1 <- AllSubs_NeuralActivation_Comedy$Theaters_US_W1_num

Horror_Gross_W1_log <- log(AllSubs_NeuralActivation_Horror$Gross_US_W1_num)
Horror_Theaters_W1 <- AllSubs_NeuralActivation_Horror$Theaters_US_W1_num
  
M1_df <- data.frame(All_Gross_W1_log, All_Theaters_W1) 
M1_C_df <- data.frame(Comedy_Gross_W1_log, Comedy_Theaters_W1) 
M1_H_df <- data.frame(Horror_Gross_W1_log, Horror_Theaters_W1) 

# Define affect variables.
All_PA <- AllSubs_NeuralActivation$Pos_arousal_scaled
All_NA <- AllSubs_NeuralActivation$Neg_arousal_scaled

Comedy_PA <- AllSubs_NeuralActivation_Comedy$Pos_arousal_scaled
Comedy_NA <- AllSubs_NeuralActivation_Comedy$Neg_arousal_scaled

Horror_PA <- AllSubs_NeuralActivation_Horror$Pos_arousal_scaled
Horror_NA <- AllSubs_NeuralActivation_Horror$Neg_arousal_scaled

M2_df <- data.frame(All_Gross_W1_log, All_PA, All_NA) 
M2_C_df <- data.frame(Comedy_Gross_W1_log, Comedy_PA, Comedy_NA) 
M2_H_df <- data.frame(Horror_Gross_W1_log, Horror_PA, Horror_NA) 
```

```{r}
# Define ISC variables. 
All_NAcc_ISC <- AllSubs_NeuralActivation$NAcc_ISC
All_AIns_ISC <- AllSubs_NeuralActivation$AIns_ISC
All_MPFC_ISC <- AllSubs_NeuralActivation$MPFC_ISC

Comedy_NAcc_ISC <- AllSubs_NeuralActivation_Comedy$NAcc_ISC
Comedy_AIns_ISC <- AllSubs_NeuralActivation_Comedy$AIns_ISC
Comedy_MPFC_ISC <- AllSubs_NeuralActivation_Comedy$MPFC_ISC

Horror_NAcc_ISC <- AllSubs_NeuralActivation_Horror$NAcc_ISC
Horror_AIns_ISC <- AllSubs_NeuralActivation_Horror$AIns_ISC
Horror_MPFC_ISC <- AllSubs_NeuralActivation_Horror$MPFC_ISC

# Define models. 
M4_df <- data.frame(All_NAcc_ISC, All_AIns_ISC, All_MPFC_ISC) 
M4_C_df <- data.frame(Comedy_NAcc_ISC, Comedy_AIns_ISC, Comedy_MPFC_ISC) 
M4_H_df <- data.frame(Horror_NAcc_ISC, Horror_AIns_ISC, Horror_MPFC_ISC) 

M5_df <- data.frame(All_Gross_W1_log, All_PA, All_NA, All_NAcc_ISC, All_AIns_ISC, All_MPFC_ISC) 
M5_C_df <- data.frame(Comedy_Gross_W1_log, Comedy_PA, Comedy_NA, Comedy_NAcc_ISC, Comedy_AIns_ISC, Comedy_MPFC_ISC) 
M5_H_df <- data.frame(Horror_Gross_W1_log, Horror_PA, Horror_NA, Horror_NAcc_ISC, Horror_AIns_ISC, Horror_MPFC_ISC) 
```

```{r}
# Define whole variables. 
All_NAcc_whole <- AllSubs_NeuralActivation$NAcc_whole
All_AIns_whole <- AllSubs_NeuralActivation$AIns_whole
All_MPFC_whole <- AllSubs_NeuralActivation$MPFC_whole

Comedy_NAcc_whole <- AllSubs_NeuralActivation_Comedy$NAcc_whole
Comedy_AIns_whole <- AllSubs_NeuralActivation_Comedy$AIns_whole
Comedy_MPFC_whole <- AllSubs_NeuralActivation_Comedy$MPFC_whole

Horror_NAcc_whole <- AllSubs_NeuralActivation_Horror$NAcc_whole
Horror_AIns_whole <- AllSubs_NeuralActivation_Horror$AIns_whole
Horror_MPFC_whole <- AllSubs_NeuralActivation_Horror$MPFC_whole

# Define models. 
M6_df <- data.frame(All_NAcc_whole, All_AIns_whole, All_MPFC_whole) 
M6_C_df <- data.frame(Comedy_NAcc_whole, Comedy_AIns_whole, Comedy_MPFC_whole) 
M6_H_df <- data.frame(Horror_NAcc_whole, Horror_AIns_whole, Horror_MPFC_whole) 

M7_df <- data.frame(All_Gross_W1_log, All_PA, All_NA, All_NAcc_whole, All_AIns_whole, All_MPFC_whole) 
M7_C_df <- data.frame(Comedy_Gross_W1_log, Comedy_PA, Comedy_NA, Comedy_NAcc_whole,
                      Comedy_AIns_whole, Comedy_MPFC_whole) 
M7_H_df <- data.frame(Horror_Gross_W1_log, Horror_PA, Horror_NA, Horror_NAcc_whole,
                      Horror_AIns_whole, Horror_MPFC_whole) 
```

```{r}
# Define onset variables. 
All_NAcc_onset <- AllSubs_NeuralActivation$NAcc_onset
All_AIns_onset <- AllSubs_NeuralActivation$AIns_onset
All_MPFC_onset <- AllSubs_NeuralActivation$MPFC_onset

Comedy_NAcc_onset <- AllSubs_NeuralActivation_Comedy$NAcc_onset
Comedy_AIns_onset <- AllSubs_NeuralActivation_Comedy$AIns_onset
Comedy_MPFC_onset <- AllSubs_NeuralActivation_Comedy$MPFC_onset

Horror_NAcc_onset <- AllSubs_NeuralActivation_Horror$NAcc_onset
Horror_AIns_onset <- AllSubs_NeuralActivation_Horror$AIns_onset
Horror_MPFC_onset <- AllSubs_NeuralActivation_Horror$MPFC_onset

# Define models. 
M8_df <- data.frame(All_NAcc_onset, All_AIns_onset, All_MPFC_onset) 
M8_C_df <- data.frame(Comedy_NAcc_onset, Comedy_AIns_onset, Comedy_MPFC_onset) 
M8_H_df <- data.frame(Horror_NAcc_onset, Horror_AIns_onset, Horror_MPFC_onset) 

M9_df <- data.frame(All_Gross_W1_log, All_PA, All_NA, All_NAcc_onset, All_AIns_onset, All_MPFC_onset) 
M9_C_df <- data.frame(Comedy_Gross_W1_log, Comedy_PA, Comedy_NA, Comedy_NAcc_onset,
                      Comedy_AIns_onset, Comedy_MPFC_onset) 
M9_H_df <- data.frame(Horror_Gross_W1_log, Horror_PA, Horror_NA, Horror_NAcc_onset,
                      Horror_AIns_onset, Horror_MPFC_onset) 
```

```{r}
# Define middle variables. 
All_NAcc_middle <- AllSubs_NeuralActivation$NAcc_middle
All_AIns_middle <- AllSubs_NeuralActivation$AIns_middle
All_MPFC_middle <- AllSubs_NeuralActivation$MPFC_middle

Comedy_NAcc_middle <- AllSubs_NeuralActivation_Comedy$NAcc_middle
Comedy_AIns_middle <- AllSubs_NeuralActivation_Comedy$AIns_middle
Comedy_MPFC_middle <- AllSubs_NeuralActivation_Comedy$MPFC_middle

Horror_NAcc_middle <- AllSubs_NeuralActivation_Horror$NAcc_middle
Horror_AIns_middle <- AllSubs_NeuralActivation_Horror$AIns_middle
Horror_MPFC_middle <- AllSubs_NeuralActivation_Horror$MPFC_middle

# Define models. 
M10_df <- data.frame(All_NAcc_middle, All_AIns_middle, All_MPFC_middle) 
M10_C_df <- data.frame(Comedy_NAcc_middle, Comedy_AIns_middle, Comedy_MPFC_middle) 
M10_H_df <- data.frame(Horror_NAcc_middle, Horror_AIns_middle, Horror_MPFC_middle) 

M11_df <- data.frame(All_Gross_W1_log, All_PA, All_NA, All_NAcc_middle, All_AIns_middle, All_MPFC_middle) 
M11_C_df <- data.frame(Comedy_Gross_W1_log, Comedy_PA, Comedy_NA, Comedy_NAcc_middle,
                      Comedy_AIns_middle, Comedy_MPFC_middle) 
M11_H_df <- data.frame(Horror_Gross_W1_log, Horror_PA, Horror_NA, Horror_NAcc_middle,
                      Horror_AIns_middle, Horror_MPFC_middle) 
```

```{r}
# Define middle variables. 
All_NAcc_offset <- AllSubs_NeuralActivation$NAcc_offset
All_AIns_offset <- AllSubs_NeuralActivation$AIns_offset
All_MPFC_offset <- AllSubs_NeuralActivation$MPFC_offset

Comedy_NAcc_offset <- AllSubs_NeuralActivation_Comedy$NAcc_offset
Comedy_AIns_offset <- AllSubs_NeuralActivation_Comedy$AIns_offset
Comedy_MPFC_offset <- AllSubs_NeuralActivation_Comedy$MPFC_offset

Horror_NAcc_offset <- AllSubs_NeuralActivation_Horror$NAcc_offset
Horror_AIns_offset <- AllSubs_NeuralActivation_Horror$AIns_offset
Horror_MPFC_offset <- AllSubs_NeuralActivation_Horror$MPFC_offset

# Define models. 
M12_df <- data.frame(All_NAcc_offset, All_AIns_offset, All_MPFC_offset) 
M12_C_df <- data.frame(Comedy_NAcc_offset, Comedy_AIns_offset, Comedy_MPFC_offset) 
M12_H_df <- data.frame(Horror_NAcc_offset, Horror_AIns_offset, Horror_MPFC_offset) 

M13_df <- data.frame(All_Gross_W1_log, All_PA, All_NA, All_NAcc_offset, All_AIns_offset, All_MPFC_offset) 
M13_C_df <- data.frame(Comedy_Gross_W1_log, Comedy_PA, Comedy_NA, Comedy_NAcc_offset,
                      Comedy_AIns_offset, Comedy_MPFC_offset) 
M13_H_df <- data.frame(Horror_Gross_W1_log, Horror_PA, Horror_NA, Horror_NAcc_offset,
                      Horror_AIns_offset, Horror_MPFC_offset) 
```

```{r}

M14_df <- data.frame(All_Gross_W1_log, All_PA, All_NA, All_NAcc_onset, All_AIns_middle, All_MPFC_offset) 
M14_C_df <- data.frame(Comedy_Gross_W1_log, Comedy_PA, Comedy_NA, Comedy_NAcc_onset,
                      Comedy_AIns_middle, Comedy_MPFC_offset) 
M14_H_df <- data.frame(Horror_Gross_W1_log, Horror_PA, Horror_NA, Horror_NAcc_onset,
                      Horror_AIns_middle, Horror_MPFC_offset) 
```


# Neuroforecasting: First Week US.
## M1: Behavioral data 
```{r, echo = FALSE}
M1 <- lm(log(Gross_US_W1_num) ~ Type 
         + scale(Theaters_US_W1_num)
         + Type:scale(Theaters_US_W1_num)
         , data = AllSubs_NeuralActivation %>% mutate(Type = factor(Type, levels = c("horror", "comedy"))))
summary(M1)
r.squaredGLMM(M1)
AIC(M1)

# Create pairs plot. 
ggpairs(M1_df)
ggpairs(M1_C_df)
ggpairs(M1_H_df)
```


## M2: Affective data alone
```{r, echo = FALSE}
M2 <- lm(log(Gross_US_W1_num) ~ Type 
         + scale(Pos_arousal_scaled) 
         + scale(Neg_arousal_scaled)
         + Type:scale(Pos_arousal_scaled)
         + Type:scale(Neg_arousal_scaled)
         , data = AllSubs_NeuralActivation %>% mutate(Type = factor(Type, levels = c("horror", "comedy"))))
summary(M2)
r.squaredGLMM(M2)
AIC(M2)

# Create pairs plot. 
ggpairs(M2_df)
ggpairs(M2_C_df)
ggpairs(M2_H_df)
```

## M3: Aggregate and affective data alone
```{r, echo = FALSE}
M3 <- lm(log(Gross_US_W1_num) ~ Type 
         #+ scale(Theaters_US_W1_num)
         + scale(Pos_arousal_scaled) 
         + scale(Neg_arousal_scaled)
         #+ Type:scale(Theaters_US_W1_num)
         + Type:scale(Pos_arousal_scaled)
         + Type:scale(Neg_arousal_scaled)
         , data = AllSubs_NeuralActivation %>% mutate(Type = factor(Type, levels = c("horror", "comedy"))))
summary(M3)
r.squaredGLMM(M3)
AIC(M3)

```

# M4: ISC data alone
```{r, echo = FALSE}
M4 <- lm(log(Gross_US_W1_num) ~ Type + 
              + scale(NAcc_ISC) 
              + scale(AIns_ISC) 
              + scale(MPFC_ISC) 
              + Type:scale(NAcc_ISC) 
              + Type:scale(AIns_ISC) 
              + Type:scale(MPFC_ISC) 
              , data = AllSubs_NeuralActivation %>% mutate(Type = factor(Type, levels = c("horror", "comedy"))))
summary(M4)
r.squaredGLMM(M4)
AIC(M4)

# Create pairs plot. 
ggpairs(M4_df)
ggpairs(M4_C_df)
ggpairs(M4_H_df)
```

# M5: ISC data + affective data + behavioral data
```{r, echo = FALSE}
M5 <- lm(log(Gross_US_W1_num) ~ Type 
             + scale(Theaters_US_W1_num) 
             + scale(Pos_arousal_scaled) 
             + scale(Neg_arousal_scaled)  
             #+ scale(W_score_scaled) 
             + scale(NAcc_ISC) 
             + scale(AIns_ISC) 
             + scale(MPFC_ISC) 
             + Type:scale(Theaters_US_W1_num) 
             + Type:scale(Pos_arousal_scaled)
             + Type:scale(Neg_arousal_scaled)
             #+ Type:scale(W_score_scaled)
             + Type:scale(NAcc_ISC) 
             + Type:scale(AIns_ISC) 
             + Type:scale(MPFC_ISC)
             , data = AllSubs_NeuralActivation %>% mutate(Type = factor(Type, levels = c("horror", "comedy"))))
summary(M5)
r.squaredGLMM(M5)
AIC(M5)

# Create pairs plot. 
ggpairs(M5_df)
ggpairs(M5_C_df)
ggpairs(M5_H_df)
```

# M6: Neural whole data alone
```{r, echo = FALSE}
M6 <- lm(log(Gross_US_W1_num) ~ Type + 
              #+ Theaters_US_W1_num 
              + scale(NAcc_whole) 
              + scale(AIns_whole) 
              + scale(MPFC_whole) 
              + Type:scale(NAcc_whole) 
              + Type:scale(AIns_whole) 
              + Type:scale(MPFC_whole) 
              , data = AllSubs_NeuralActivation %>% mutate(Type = factor(Type, levels = c("horror", "comedy"))))
summary(M6)
r.squaredGLMM(M6)
AIC(M6)

# Create pairs plot. 
ggpairs(M6_df)
ggpairs(M6_C_df)
ggpairs(M6_H_df)
```

# M7: Neural whole data + affective data + behavioral data
```{r, echo = FALSE}
M7 <- lm(log(Gross_US_W1_num) ~ Type 
             + scale(Theaters_US_W1_num)
             #+ Total_weeks 
             #+ Weeks_avg_per_theater
             + scale(Pos_arousal_scaled) 
             + scale(Neg_arousal_scaled)  
             #+ scale(W_score_scaled) 
             + scale(NAcc_whole) 
             + scale(AIns_whole) 
             + scale(MPFC_whole) 
             + Type:scale(Theaters_US_W1_num)
             + Type:scale(Pos_arousal_scaled)
             + Type:scale(Neg_arousal_scaled)
             + Type:scale(NAcc_whole) 
             + Type:scale(AIns_whole) 
             + Type:scale(MPFC_whole)
             , data = AllSubs_NeuralActivation %>% mutate(Type = factor(Type, levels = c("horror", "comedy"))))
summary(M7)
r.squaredGLMM(M7)
AIC(M7)

# Create pairs plot. 
ggpairs(M7_df)
ggpairs(M7_C_df)
ggpairs(M7_H_df)
```

# M8: Neural onset data alone
```{r, echo = FALSE}
M8 <- lm(log(Gross_US_W1_num) ~ Type + 
              + scale(NAcc_onset) 
              + scale(AIns_onset) 
              + scale(MPFC_onset) 
              + Type:scale(NAcc_onset) 
              + Type:scale(AIns_onset) 
              + Type:scale(MPFC_onset) 
              , data = AllSubs_NeuralActivation %>% mutate(Type = factor(Type, levels = c("horror", "comedy"))))
summary(M8)
r.squaredGLMM(M8)
AIC(M8)

# Create pairs plot. 
ggpairs(M8_df)
ggpairs(M8_C_df)
ggpairs(M8_H_df)
```

# M9: Neural onset data + affective data + behavioral data
```{r, echo = FALSE}
M9 <- lm(log(Gross_US_W1_num) ~ Type 
             + scale(Theaters_US_W1_num)
             + scale(Pos_arousal_scaled) 
             + scale(Neg_arousal_scaled)  
             + scale(NAcc_onset) 
             + scale(AIns_onset) 
             + scale(MPFC_onset) 
             + Type:scale(Theaters_US_W1_num)
             + Type:scale(Pos_arousal_scaled)
             + Type:scale(Neg_arousal_scaled)
             + Type:scale(NAcc_onset) 
             + Type:scale(AIns_onset) 
             + Type:scale(MPFC_onset)
             , data = AllSubs_NeuralActivation %>% mutate(Type = factor(Type, levels = c("horror", "comedy"))))
summary(M9)
r.squaredGLMM(M9)
AIC(M9)

# Create pairs plot. 
ggpairs(M9_df)
ggpairs(M9_C_df)
ggpairs(M9_H_df)
```

# M10: Neural middle data alone
```{r, echo = FALSE}
M10 <- lm(log(Gross_US_W1_num) ~ Type + 
              + scale(NAcc_middle) 
              + scale(AIns_middle) 
              + scale(MPFC_middle) 
              + Type:scale(NAcc_middle) 
              + Type:scale(AIns_middle) 
              + Type:scale(MPFC_middle) 
              , data = AllSubs_NeuralActivation %>% mutate(Type = factor(Type, levels = c("horror", "comedy"))))
summary(M10)
r.squaredGLMM(M10)
AIC(M10)

# Create pairs plot. 
ggpairs(M10_df)
ggpairs(M10_C_df)
ggpairs(M10_H_df)
```

# M11: Neural middle data + affective data + behavioral data
```{r, echo = FALSE}
M11 <- lm(log(Gross_US_W1_num) ~ Type 
             + scale(Theaters_US_W1_num)
             + scale(Pos_arousal_scaled) 
             + scale(Neg_arousal_scaled)  
             + scale(NAcc_middle) 
             + scale(AIns_middle) 
             + scale(MPFC_middle) 
             + Type:scale(Theaters_US_W1_num)
             + Type:scale(Pos_arousal_scaled)
             + Type:scale(Neg_arousal_scaled)
             + Type:scale(NAcc_middle) 
             + Type:scale(AIns_middle) 
             + Type:scale(MPFC_middle)
             , data = AllSubs_NeuralActivation %>% mutate(Type = factor(Type, levels = c("horror", "comedy"))))
summary(M11)
r.squaredGLMM(M11)
AIC(M11)

# Create pairs plot. 
ggpairs(M11_df)
ggpairs(M11_C_df)
ggpairs(M11_H_df)
```

# M12: Neural offset data alone
```{r, echo = FALSE}
M12 <- lm(log(Gross_US_W1_num) ~ Type + 
              + scale(NAcc_offset) 
              + scale(AIns_offset) 
              + scale(MPFC_offset) 
              + Type:scale(NAcc_offset) 
              + Type:scale(AIns_offset) 
              + Type:scale(MPFC_offset) 
              , data = AllSubs_NeuralActivation %>% mutate(Type = factor(Type, levels = c("horror", "comedy"))))
summary(M12)
r.squaredGLMM(M12)
AIC(M12)

# Create pairs plot. 
ggpairs(M12_df)
ggpairs(M12_C_df)
ggpairs(M12_H_df)
```

# M13: Neural offset data + affective data + behavioral data
```{r, echo = FALSE}
M13 <- lm(log(Gross_US_W1_num) ~ Type 
             + scale(Theaters_US_W1_num)
             + scale(Pos_arousal_scaled) 
             + scale(Neg_arousal_scaled)  
             + scale(NAcc_offset) 
             + scale(AIns_offset) 
             + scale(MPFC_offset) 
             + Type:scale(Theaters_US_W1_num)
             + Type:scale(Pos_arousal_scaled)
             + Type:scale(Neg_arousal_scaled)
             + Type:scale(NAcc_offset) 
             + Type:scale(AIns_offset) 
             + Type:scale(MPFC_offset)
             , data = AllSubs_NeuralActivation %>% mutate(Type = factor(Type, levels = c("horror", "comedy"))))
summary(M13)
r.squaredGLMM(M13)
AIC(M13)

# Create pairs plot. 
ggpairs(M13_df)
ggpairs(M13_C_df)
ggpairs(M13_H_df)
```

# M14: Sequence model
```{r, echo = FALSE}
M14 <- lm(log(Gross_US_W1_num) ~ Type 
             + scale(Theaters_US_W1_num)
             + scale(Pos_arousal_scaled) 
             + scale(Neg_arousal_scaled) 
             #+ scale(W_score_scaled)  
             + scale(NAcc_onset) 
             + scale(AIns_middle) 
             + scale(MPFC_offset) 
             #+ Type:scale(Theaters_US_W1_num) # This improved the results. 
             + Type:scale(Pos_arousal_scaled)
             + Type:scale(Neg_arousal_scaled)
             #+ Type:scale(W_score_scaled)
             + Type:scale(NAcc_onset) 
             + Type:scale(AIns_middle) 
             + Type:scale(MPFC_offset)
             , data = AllSubs_NeuralActivation %>% mutate(Type = factor(Type, levels = c("horror", "comedy"))))
summary(M14)
r.squaredGLMM(M14)
AIC(M14)

# Create pairs plot. 
#ggpairs(M14_df)
#ggpairs(M14_C_df)
#ggpairs(M14_H_df)
vif(M14)
```
